{"id":"W2169538102","doi":"10.1109/ccece.2005.1556888","title":"AM-FM analysis of a chirp multicomponent signal employing MWL criterion","year":2006,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Chirp; SIGNAL (programming language); Amplitude; Chirp spread spectrum; Algorithm; Bandwidth (computing); Computer science; Component (thermodynamics); Frequency modulation; Range (aeronautics); Time–frequency analysis; Instantaneous phase; Mathematics; Telecommunications; Physics; Spread spectrum; Optics; Engineering; Direct-sequence spread spectrum; Radar","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001433774,0.0001005218,0.0002231259,0.0003311574,0.00008596905,0.0001270593,0.0003908477,0.00003087051,0.00006562675],"category_scores_gemma":[0.000006979424,0.00008393224,0.0001385266,0.0009621197,0.00002661167,0.000313195,0.0001206787,0.00005242098,0.00001021799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002157223,"about_ca_system_score_gemma":0.0000188334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003299902,"about_ca_topic_score_gemma":0.0000485783,"domain_scores_codex":[0.9989557,0.00002533975,0.0002746536,0.0002724503,0.0002590102,0.0002128242],"domain_scores_gemma":[0.9994705,0.00004184159,0.0001054863,0.0002669923,0.00006902547,0.00004612551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001446731,0.0004327019,0.05772388,0.00005011287,0.0003373875,0.00003264476,0.0005622093,0.007060995,0.8191667,0.003597333,0.0003325492,0.1106891],"study_design_scores_gemma":[0.0003177921,0.00004314927,0.09420522,0.00002603877,0.0001317011,0.000004332179,0.00002516787,0.3029386,0.6007966,0.001061193,0.0002093705,0.0002407676],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4079821,0.00006008473,0.5902224,0.0001467408,0.00003456446,0.00002956094,7.617801e-7,0.000083712,0.001440098],"genre_scores_gemma":[0.8882089,0.000001020716,0.1114901,0.0001210754,0.0000323198,0.000001510962,0.000003826946,0.000003787643,0.0001375309],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4802267,"threshold_uncertainty_score":0.3422657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01153063358450791,"score_gpt":0.2433957398060284,"score_spread":0.2318651062215205,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}